Rate-based multivariable control with stability assurance

ABSTRACT

Method, system and computer executable instructions for controlling a process that include collecting data sets for a process having an initial base layer DCV value and a corresponding initial ICV value, a rate-time value and a move series value, receiving a target ICV value and determining whether a change in DCV value is needed. The method checks for any DCV limits and implements a move series to the DCV setting. The rate-of-change of the ICV is calculated along with the estimated time needed to reach the target ICV value based on the rate-of-change. The time needed to reach the target ICV value is compared with the rate-time and the DCV move series is discontinued when the time needed to reach the target ICV value is less than a predetermined percentage of the rate-time value.

FIELD

The present invention is generally related to the process industries,which include mineral and oil refining, chemical and petrochemicalproduction, fossil and nuclear power generation, parts of the food andpharmaceutical industries, etc. More specifically, the present inventionis related to methods of controlling a process. The method enables amore reliable and safer process control than obtainable under the priorart.

BACKGROUND

Process industries, which include mineral and oil refining, chemical andpetrochemical production, fossil and nuclear power generation, parts ofthe food and pharmaceutical industries to name a few, are generallycharacterized by continuous fluid processing, with unit operations suchas reactions, purification, and energy exchange. This stands in contrastwith discrete parts manufacturing industries, such as electronics orautomobile manufacture.

Process control systems generally consist of many direct controlvariables, such as flow controllers, temperature controllers, andpressure controllers, and also many indirect control variables, such astemperatures, pressures and flows that are not directly controlled, butwhich are affected by various process influences, including the directcontrollers. The multiple interactions between the direct controllers(as well as other independent variables) and the indirect variables (ordependent variables) comprise the multivariable nature of mostcontinuous processes.

Prior to the advent of computer-based process control systems, ca.1980s, multivariable control, i.e. controlling direct and indirectcontrol variables in a coordinated manner, was accomplished manually. Itwas part of the operator's job to adjust the direct controllers in orderto keep them and the indirect variables within specified constraintlimits, as well as to affect greater economic optimization, for example,to make incrementally more product, or higher quality product, or makemore efficient use of raw materials and energy.

Due to the degree of unwieldiness of most industrial scale processes,their susceptibility to many sources of upsets, the often sharp safetyand cost consequences of exceeding constraints, and in the absence ofautomatic multivariable constraint controls, process operationtraditionally is kept well away from critical constraints. However, inmost cases, operating away from constraints translates into additionaloperating expense, i.e. results in making less product or consuming moreenergy or raw materials. This is an essential aspect of operating mostindustrial scale continuous processes—keeping the process within a safeand reliable operating window, while pushing overall process economics.

With the advent of computer-based control systems and appropriatecontrol methods in the 1980s, automated multivariable control becameestablished as a viable and often important part of modern processcontrol systems. With automated multivariable control, many processesare able to operate reliably closer to constraint limits andoptimization targets, with potentially significant benefits in productquality, efficiency, throughput, reliability and safety.

In the process industries, the dominant method of multivariable controlhas been multivariable model-based predictive control (MPC). “Models”are mathematical descriptions of the process interactions between thedirect control variables and the indirect control variables (or, betweenthe independent and dependent variables). MPC uses models in itsconstraint control and optimization algorithms (along with cost andother factors). Since the 1980s, thousands of instances of MPC have beendeployed in industry.

Its success aside, MPC has experienced several persistent shortcomings.The inventor has been a leading industry voice for understanding andimproving MPC effectiveness, having published numerous trade journalarticles on the topic, and has generally concluded that model-basedcontrol has a fundamental weakness in that models are inherentlyinaccurate (because actual process gains change dynamically), andmoreover that the aggressive nature of model-based control isunnecessary and even undesirable from a process operation standpoint,where gradual constraint control and optimization is more prudent andassuring process stability is a high priority.

Multivariable control has been an active technology area in the processindustries. However, to the inventor's knowledge all prior patentsrelate to methods of model-based control and/or optimization methods.The present invention is a method for multivariable control that is notmodel-based, and which is independent of any optimizer method employed.A search has revealed no other patents for a non-model-based approach tomultivariable control.

In response, the inventor has developed the present invention, whichinherently adapts to changes in process gain, yet is still predictiveand accomplishes multivariable constraint control and optimization in agradual manner that assures ongoing process stability, in addition toseveral other control and operating advantages that are described.

SUMMARY

Disclosed herein is a method of process control that includes collectingdata sets for a process having an initial base layer DCV value and acorresponding initial ICV value, a rate-time value and a move seriesvalue, receiving a target ICV value and determining whether a change inDCV value is needed. The method checks for any DCV limits and implementsa move series to the DCV setting. The rate-of-change of the ICV iscalculated along with the estimated time needed to reach the target ICVvalue based on the rate-of-change. The time needed to reach the targetICV value is compared with the rate-time and the DCV move series isdiscontinued when the time needed to reach the target ICV value is lessthan a predetermined percentage of the rate-time value. The method canfurther include checking process stability and if an unstable conditionwithin the process is detected the move series can be temporarily halteduntil stability is achieved.

While the prior art of multivariable process control employs highlycomplex and often unreliable calculation methods to arrive atindependent variable control “moves”, the present invention determinesonly if a positive or negative control move is needed, and then uses afixed and continuous move size that is pre-selected based on processsafety and stability criteria. This method reduces the complexity andimproves the reliability of the move calculation process, and moreclosely reflects how operating personnel usually prefer to operateindustrial processes, i.e. with cautious gradual moves to assure ongoingprocess stability.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is schematic of a simplified example of a process model matrix.Each mini-graph reflects the response of the ICV to a change in the DCV.

FIG. 2 is a schematic of a typical distributed control system (DCS)architecture in common use in the prior art.

FIGS. 3a through 3f are graphical illustrations from mathematicalprocess simulations of the present invention, illustrating the effectsof changing process gain, response time, and move size.

FIG. 4 is a block diagram of the present invention method of rate-basedcontrol with stability assurance, using an example of controlling oneICV with one DCV.

FIG. 5 is a diagrammatic representation of a machine in an example formof a computer system within which a set of instructions for causing themachine to perform any one or more of the methodologies discussed hereinmay be executed.

DETAILED DESCRIPTION

The subject matter of the present invention is described withspecificity, however, the description itself is not intended to limitthe scope of the invention. The subject matter thus, might also beembodied in other ways, to include different steps or combinations ofsteps similar to the ones described herein, in conjunction with otherpresent or future technologies. Moreover, although the term “step” maybe used herein to describe different elements of methods employed, theterm should not be interpreted as implying any particular order among orbetween various steps herein disclosed unless otherwise expresslylimited by the description to a particular order. While the followingdescription refers to the process industry, the systems and methods ofthe present invention are not limited thereto and may also be applied toother industries to achieve similar results.

This detailed description explains how an embodiment of the presentinvention, termed rate-based multivariable control (RMC), uses onedirect control variable (DCV) to control one indirect control variable(ICV). The method can further be used broadly to effect multivariablecontrol of an entire process comprised of multiple DCVs and ICVs.

In a generalized embodiment the present invention comprises a processcontrol method that determines whether a control move is needed,implements a first move series, monitors a dependent variablerate-of-change and halts the move series whenever the dependent variablerate-of-change indicates that the dependent variable target will be metwithin a specified time period. The method can further monitor processstability and discontinue the control method when instability isdetected.

An exemplary embodiment is a method of process control that includescollecting data sets for a process having an initial base layer DCVvalue and a corresponding initial ICV value, a rate-time value and amove series value. A target ICV value is received and whether a changein DCV value is needed is determined. The method checks for any DCVlimits and implements a move series to the DCV setting to effect thechange. The rate-of-change of the ICV is calculated along with theestimated time needed to reach the target ICV value based on therate-of-change. The time needed to reach the target ICV value iscompared with the rate-time and the DCV move series is discontinued whenthe time needed to reach the target ICV value is less than apredetermined percentage of the rate-time value. The method can furtherinclude checking process stability and if an unstable condition withinthe process is detected the move series can be temporarily halted untilstability is achieved.

While the prior art of multivariable process control employs highlycomplex and often unreliable calculation methods to arrive atindependent variable control “moves”, the present invention determinesonly if a positive or negative control move is needed, and then uses afixed and continuous move size that is pre-selected based on processsafety and stability criteria. This method reduces the complexity andimproves the reliability of the move calculation process, and moreclosely reflects how operating personnel usually prefer to operateindustrial processes, i.e. with cautious gradual moves to assure ongoingprocess stability.

Those knowledgeable in the art will recognize that this approachrequires some complementary mechanism to halt the moves before thedependent variable actually reaches its target value, so that it willsettle at the target value without overshoot or cycling. This isaccomplished by real-time monitoring of the dependent variablerate-of-change and halting the moves when process conditions indicatethat doing so will result in the dependent variable ultimately settlingout at the target value. This method is less complex and more reliablethan the methods of the prior art, and more closely reflects howoperating personnel generally prefer to operate industrial processes.Moreover, process simulations show that this method is relatively robust(insensitive) with regard to changes in process response time, and isunaffected by changes in process gain and move size. These are importantcharacteristics from a control and operation standpoint, and have been aparticular shortcoming of the prior art.

The method of the present invention for multivariable control, or any“high level” process control application, is made more reliable by theaddition of stability assurance, meaning process stability iscontinuously monitored and the high level control application is pausedor automatically switched off whenever instability is detected. Becausehigh level control is generally intended to enhance process operation,the idea that it should be turned off during instability has never beena part of process control practice. However, the history of the priorart of multivariable control, upon study by the inventor, has revealedthe prudence and logic of this safeguarding feature for any high levelprocess control application, because in practice, the prior art, due toa number of practical shortcomings, has been known to actually causeprocess instability.

In an example the DCV is initially at a value of 20%. In an actualprocess, this could represent a flow controller setpoint. The ICV has aninitial value equal to its target value of 60%. In an actual process,this could be a maximum pressure constraint or a product quality target.At time equals ten minutes, the ICV target changes to 90%. In an actualprocess this could be a change made by an operator, engineer, or anautomatic optimizer. The gain describing the response of the ICV tochanges in the DCV is positive, meaning an increase in the DCV willcause an increase in the ICV, therefore the DCV needs to be increased tobring the ICV up to the new target value.

The process response time in this example is twenty minutes, i.e. aftera change in the DCV, the ICV will reach its final value in about sixtyminutes (roughly three response times). The process response time is thebasis for the RMC tuning parameter rate-time. Rate-time is used withinRMC as follows: (1) if the current rate of change of the ICV indicatesit will not reach its target value within a time period that is lessthan or equal to the rate-time, then further moves are continued; and(2) if the current rate of change of the ICV indicates it will reach itstarget value within a time period that is less than or equal to therate-time, then further moves are discontinued, allowing the ICV tosettle towards the target value. Optionally the time to reach its targetvalue can be compared to a percentage of the rate-time, for example themove can be discontinued when the time to reach the target value isequal to 110% of the rate-time.

In an ideally behaved mathematical simulation, if the process responsetime equals the rate-time, then when the moves are initiallydiscontinued, the ICV will subsequently settle at exactly the targetvalue. This is termed the “ideal” rate-time, although in practice therate-time may be set longer or shorter than the process response time,or may become longer or shorter if the process response time changesdynamically.

FIGS. 3a through 3f illustrate the effect of process gain, processresponse time, rate-time, and move size on RMC control performance, andin particular illustrate that the ideal rate-time is unaffected bychanges in process gain and move size, and is only mildly affected bychanges in process response time.

FIG. 3a illustrates an idealized case. In this simulation, gain is 1.0,move size is 1.0, and rate-time equals the process response time of 20minutes. When the target increases from 60% to 90% at time=10, thecontroller commences a DCV move series of +1.0/minute. The graph showsthe target increase, the increasing DCV, and the ICV response. Attime=40, the actual rate of change of the ICV (basically the slope ofthe ICV trace) indicates it will reach its target within one rate-time(within 20 minutes), so the move series is discontinued and the ICVultimately settles at the target value three response times later(time=ca. 100 minutes). In this idealized case, it can be foreseen thatthe DCV move series will complete at a value of 50 and at time=40,because with a gain of 1.0 and a target change of 30, the DCV will needto increase by a total of 30, which occurs over a 30 minute period witha move size of 1.0. The simulation bears this out. Details of thesimulation shown in FIG. 3a are listed in Table 1. It can be seen thatthe move series is discontinued after minute 40 when the velstop valueexceeds the target value of 90.

TABLE 1 final min mov DCV ICV Target icvvel veldist velstop icv 0 0 20.060.00 60 0.00 0.00 60.00 60.00 1 0 20.0 60.00 60 0.00 0.00 60.00 60.00 20 20.0 60.00 60 0.00 0.00 60.00 60.00 3 0 20.0 60.00 60 0.00 0.00 60.0060.00 4 0 20.0 60.00 60 0.00 0.00 60.00 60.00 5 0 20.0 60.00 60 0.000.00 60.00 60.00 6 0 20.0 60.00 60 0.00 0.00 60.00 60.00 7 0 20.0 60.0060 0.00 0.00 60.00 60.00 8 0 20.0 60.00 60 0.00 0.00 60.00 60.00 9 020.0 60.00 60 0.00 0.00 60.00 60.00 10 0 20.0 60.00 90 0.00 0.00 60.0060.00 11 1 21.0 60.05 90 0.05 1.00 61.05 61.00 12 1 22.0 60.15 90 0.101.95 62.10 62.00 13 1 23.0 60.29 90 0.14 2.85 63.14 63.00 14 1 24.060.48 90 0.19 3.71 64.19 64.00 15 1 25.0 60.70 90 0.23 4.52 65.23 65.0016 1 26.0 60.97 90 0.26 5.30 66.26 66.00 17 1 27.0 61.27 90 0.30 6.0367.30 67.00 18 1 28.0 61.60 90 0.34 6.73 68.34 68.00 19 1 29.0 61.97 900.37 7.40 69.37 69.00 20 1 30.0 62.38 90 0.40 8.03 70.40 70.00 21 1 31.062.81 90 0.43 8.62 71.43 71.00 22 1 32.0 63.27 90 0.46 9.19 72.46 72.0023 1 33.0 63.75 90 0.49 9.73 73.49 73.00 24 1 34.0 64.27 90 0.51 10.2574.51 74.00 25 1 35.0 64.80 90 0.54 10.73 75.54 75.00 26 1 36.0 65.36 900.56 11.20 76.56 76.00 27 1 37.0 65.94 90 0.58 11.64 77.58 77.00 28 138.0 66.55 90 0.60 12.06 78.60 78.00 29 1 39.0 67.17 90 0.62 12.45 79.6279.00 30 1 40.0 67.81 90 0.64 12.83 80.64 80.00 31 1 41.0 68.47 90 0.6613.19 81.66 81.00 32 1 42.0 69.15 90 0.68 13.53 82.68 82.00 33 1 43.069.84 90 0.69 13.85 83.69 83.00 34 1 44.0 70.55 90 0.71 14.16 84.7184.00 35 1 45.0 71.27 90 0.72 14.45 85.72 85.00 36 1 46.0 72.01 90 0.7414.73 86.74 86.00 37 1 47.0 72.76 90 0.75 14.99 87.75 87.00 38 1 48.073.52 90 0.76 15.24 88.76 88.00 39 1 49.0 74.29 90 0.77 15.48 89.7789.00 40 1 50.0 75.08 90 0.79 15.71 90.79 90.00 41 0 50.0 75.82 90 0.7514.92 90.75 90.00 42 0 50.0 76.53 90 0.71 14.18 90.71 90.00 43 0 50.077.21 90 0.67 13.47 90.67 90.00 44 0 50.0 77.85 90 0.64 12.79 90.6490.00 45 0 50.0 78.45 90 0.61 12.15 90.61 90.00 46 0 50.0 79.03 90 0.5811.55 90.58 90.00 47 0 50.0 79.58 90 0.55 10.97 90.55 90.00 48 0 50.080.10 90 0.52 10.42 90.52 90.00 49 0 50.0 80.60 90 0.49 9.90 90.49 90.0050 0 50.0 81.07 90 0.47 9.40 90.47 90.00 51 0 50.0 81.51 90 0.45 8.9390.45 90.00 52 0 50.0 81.94 90 0.42 8.49 90.42 90.00 53 0 50.0 82.34 900.40 8.06 90.40 90.00 54 0 50.0 82.72 90 0.38 7.66 90.38 90.00 55 0 50.083.09 90 0.36 7.28 90.36 90.00 56 0 50.0 83.43 90 0.35 6.91 90.35 90.0057 0 50.0 83.76 90 0.33 6.57 90.33 90.00 58 0 50.0 84.07 90 0.31 6.2490.31 90.00 59 0 50.0 84.37 90 0.30 5.93 90.30 90.00 60 0 50.0 84.65 900.28 5.63 90.28 90.00 61 0 50.0 84.92 90 0.27 5.35 90.27 90.00 62 0 50.085.17 90 0.25 5.08 90.25 90.00 63 0 50.0 85.41 90 0.24 4.83 90.24 90.0064 0 50.0 85.64 90 0.23 4.59 90.23 90.00 65 0 50.0 85.86 90 0.22 4.3690.22 90.00 66 0 50.0 86.07 90 0.21 4.14 90.21 90.00 67 0 50.0 86.26 900.20 3.93 90.20 90.00 68 0 50.0 86.45 90 0.19 3.74 90.19 90.00 69 0 50.086.63 90 0.18 3.55 90.18 90.00 70 0 50.0 86.80 90 0.17 3.37 90.17 90.0071 0 50.0 86.96 90 0.16 3.20 90.16 90.00 72 0 50.0 87.11 90 0.15 3.0490.15 90.00 73 0 50.0 87.25 90 0.14 2.89 90.14 90.00 74 0 50.0 87.39 900.14 2.75 90.14 90.00 75 0 50.0 87.52 90 0.13 2.61 90.13 90.00 76 0 50.087.65 90 0.12 2.48 90.12 90.00 77 0 50.0 87.76 90 0.12 2.35 90.12 90.0078 0 50.0 87.88 90 0.11 2.24 90.11 90.00 79 0 50.0 87.98 90 0.11 2.1290.11 90.00 80 0 50.0 88.08 90 0.10 2.02 90.10 90.00 81 0 50.0 88.18 900.10 1.92 90.10 90.00 82 0 50.0 88.27 90 0.09 1.82 90.09 90.00 83 0 50.088.36 90 0.09 1.73 90.09 90.00 84 0 50.0 88.44 90 0.08 1.64 90.08 90.0085 0 50.0 88.52 90 0.08 1.56 90.08 90.00 86 0 50.0 88.59 90 0.07 1.4890.07 90.00 87 0 50.0 88.66 90 0.07 1.41 90.07 90.00 88 0 50.0 88.73 900.07 1.34 90.07 90.00 89 0 50.0 88.79 90 0.06 1.27 90.06 90.00 90 0 50.088.85 90 0.06 1.21 90.06 90.00 91 0 50.0 88.91 90 0.06 1.15 90.06 90.0092 0 50.0 88.96 90 0.05 1.09 90.05 90.00 93 0 50.0 89.02 90 0.05 1.0490.05 90.00 94 0 50.0 89.06 90 0.05 0.98 90.05 90.00 95 0 50.0 89.11 900.05 0.94 90.05 90.00 96 0 50.0 89.16 90 0.04 0.89 90.04 90.00 97 0 50.089.20 90 0.04 0.84 90.04 90.00 98 0 50.0 89.24 90 0.04 0.80 90.04 90.00

FIG. 3b illustrates that the method is independent of actual processgain and in fact adapts to actual process gain in real-time. In FIG. 3b, the process gain is 2.0 (twice the original gain). This means the DCVneeds to move only half as much, which takes half as long at a constantmove size, but because rate-time still reflects process response time,the method still lands the ICV on target. This inherent adaptation to achanging process gain is an important aspect of the invention, becausechanges in process gain are a reality for most processes and afundamental problem for model-based control in the prior art. Inmodel-based control, as well as in traditional feedbackproportional-integral-derivative (PID) control, control performancedegrades rapidly, typically with increasingly severe oscillations, asprocess gain increases and generally when process gain increases beyonda factor of two, control becomes completely unstable and results inoscillations of growing, rather than decaying, amplitude. As the exampleof FIG. 3b illustrates, control performance under the present inventionis unaffected by even a doubling of process gain. Details of thesimulation shown in FIG. 3b are listed in Table 2.

TABLE 2 final min mov DCV ICV Target icvvel veldist velstop icv 0 0 20.060.00 60 0.00 0.00 60.00 60.00 1 0 20.0 60.00 60 0.00 0.00 60.00 60.00 20 20.0 60.00 60 0.00 0.00 60.00 60.00 3 0 20.0 60.00 60 0.00 0.00 60.0060.00 4 0 20.0 60.00 60 0.00 0.00 60.00 60.00 5 0 20.0 60.00 60 0.000.00 60.00 60.00 6 0 20.0 60.00 60 0.00 0.00 60.00 60.00 7 0 20.0 60.0060 0.00 0.00 60.00 60.00 8 0 20.0 60.00 60 0.00 0.00 60.00 60.00 9 020.0 60.00 60 0.00 0.00 60.00 60.00 10 0 20.0 60.00 90 0.00 0.00 60.0060.00 11 1 21.0 60.10 90 0.10 2.00 62.10 62.00 12 1 22.0 60.30 90 0.203.90 64.20 64.00 13 1 23.0 60.58 90 0.29 5.70 66.29 66.00 14 1 24.060.95 90 0.37 7.42 68.37 68.00 15 1 25.0 61.40 90 0.45 9.05 70.45 70.0016 1 26.0 61.93 90 0.53 10.60 72.53 72.00 17 1 27.0 62.54 90 0.60 12.0774.60 74.00 18 1 28.0 63.21 90 0.67 13.46 76.67 76.00 19 1 29.0 63.95 900.74 14.79 78.74 78.00 20 1 30.0 64.75 90 0.80 16.05 80.80 80.00 21 131.0 65.61 90 0.86 17.25 82.86 82.00 22 1 32.0 66.53 90 0.92 18.39 84.9284.00 23 1 33.0 67.51 90 0.97 19.47 86.97 86.00 24 1 34.0 68.53 90 1.0220.49 89.02 88.00 25 1 35.0 69.61 90 1.07 21.47 91.07 90.00 26 0 35.070.62 90 1.02 20.39 91.02 90.00 27 0 35.0 71.59 90 0.97 19.38 90.9790.00 28 0 35.0 72.51 90 0.92 18.41 90.92 90.00 29 0 35.0 73.39 90 0.8717.49 90.87 90.00 30 0 35.0 74.22 90 0.83 16.61 90.83 90.00 31 0 35.075.01 90 0.79 15.78 90.79 90.00 32 0 35.0 75.76 90 0.75 14.99 90.7590.00 33 0 35.0 76.47 90 0.71 14.24 90.71 90.00 34 0 35.0 77.15 90 0.6813.53 90.68 90.00 35 0 35.0 77.79 90 0.64 12.85 90.64 90.00 36 0 35.078.40 90 0.61 12.21 90.61 90.00 37 0 35.0 78.98 90 0.58 11.60 90.5890.00 38 0 35.0 79.53 90 0.55 11.02 90.55 90.00 39 0 35.0 80.05 90 0.5210.47 90.52 90.00 40 0 35.0 80.55 90 0.50 9.95 90.50 90.00 41 0 35.081.02 90 0.47 9.45 90.47 90.00 42 0 35.0 81.47 90 0.45 8.98 90.45 90.0043 0 35.0 81.90 90 0.43 8.53 90.43 90.00 44 0 35.0 82.30 90 0.41 8.1090.41 90.00 45 0 35.0 82.69 90 0.38 7.70 90.38 90.00 46 0 35.0 83.05 900.37 7.31 90.37 90.00 47 0 35.0 83.40 90 0.35 6.95 90.35 90.00 48 0 35.083.73 90 0.33 6.60 90.33 90.00 49 0 35.0 84.04 90 0.31 6.27 90.31 90.0050 0 35.0 84.34 90 0.30 5.96 90.30 90.00 51 0 35.0 84.63 90 0.28 5.6690.28 90.00 52 0 35.0 84.89 90 0.27 5.37 90.27 90.00 53 0 35.0 85.15 900.26 5.11 90.26 90.00 54 0 35.0 85.39 90 0.24 4.85 90.24 90.00 55 0 35.085.62 90 0.23 4.61 90.23 90.00 56 0 35.0 85.84 90 0.22 4.38 90.22 90.0057 0 35.0 86.05 90 0.21 4.16 90.21 90.00 58 0 35.0 86.25 90 0.20 3.9590.20 90.00 59 0 35.0 86.43 90 0.19 3.75 90.19 90.00 60 0 35.0 86.61 900.18 3.57 90.18 90.00 61 0 35.0 86.78 90 0.17 3.39 90.17 90.00 62 0 35.086.94 90 0.16 3.22 90.16 90.00 63 0 35.0 87.10 90 0.15 3.06 90.15 90.0064 0 35.0 87.24 90 0.15 2.90 90.15 90.00 65 0 35.0 87.38 90 0.14 2.7690.14 90.00 66 0 35.0 87.51 90 0.13 2.62 90.13 90.00 67 0 35.0 87.63 900.12 2.49 90.12 90.00 68 0 35.0 87.75 90 0.12 2.37 90.12 90.00 69 0 35.087.87 90 0.11 2.25 90.11 90.00 70 0 35.0 87.97 90 0.11 2.13 90.11 90.0071 0 35.0 88.07 90 0.10 2.03 90.10 90.00 72 0 35.0 88.17 90 0.10 1.9390.10 90.00 73 0 35.0 88.26 90 0.09 1.83 90.09 90.00 74 0 35.0 88.35 900.09 1.74 90.09 90.00 75 0 35.0 88.43 90 0.08 1.65 90.08 90.00 76 0 35.088.51 90 0.08 1.57 90.08 90.00 77 0 35.0 88.58 90 0.07 1.49 90.07 90.0078 0 35.0 88.65 90 0.07 1.42 90.07 90.00 79 0 35.0 88.72 90 0.07 1.3590.07 90.00 80 0 35.0 88.79 90 0.06 1.28 90.06 90.00 81 0 35.0 88.85 900.06 1.21 90.06 90.00 82 0 35.0 88.90 90 0.06 1.15 90.06 90.00 83 0 35.088.96 90 0.05 1.10 90.05 90.00 84 0 35.0 89.01 90 0.05 1.04 90.05 90.0085 0 35.0 89.06 90 0.05 0.99 90.05 90.00 86 0 35.0 89.11 90 0.05 0.9490.05 90.00 87 0 35.0 89.15 90 0.04 0.89 90.04 90.00 88 0 35.0 89.19 900.04 0.85 90.04 90.00 89 0 35.0 89.23 90 0.04 0.81 90.04 90.00 90 0 35.089.27 90 0.04 0.77 90.04 90.00 91 0 35.0 89.31 90 0.04 0.73 90.04 90.0092 0 35.0 89.34 90 0.03 0.69 90.03 90.00 93 0 35.0 89.38 90 0.03 0.6690.03 90.00 94 0 35.0 89.41 90 0.03 0.62 90.03 90.00 95 0 35.0 89.44 900.03 0.59 90.03 90.00 96 0 35.0 89.47 90 0.03 0.56 90.03 90.00 97 0 35.089.49 90 0.03 0.53 90.03 90.00 98 0 35.0 89.52 90 0.03 0.51 90.03 90.0099 0 35.0 89.54 90 0.02 0.48 90.02 90.00 100 0 35.0 89.56 90 0.02 0.4690.02 90.00

FIG. 3c illustrates that the method is independent of move size, whichmeans move size can be adjusted without undermining quality of control.In FIG. 3c , the move size is 2.0 (twice the original move size), sothat the DCV moves are completed in half the time, but the ICV againlands on target. This illustrates the independence of the method tochanges in move size, which is important, as this is a parameteroperations personnel may want to adjust based on ongoing operationalexperience. Details of the simulation shown in FIG. 3c are listed inTable 3.

TABLE 3 final min mov DCV ICV Target icvvel veldist velstop icv 0 0 20.060.00 60 0.00 0.00 60.00 60.00 1 0 20.0 60.00 60 0.00 0.00 60.00 60.00 20 20.0 60.00 60 0.00 0.00 60.00 60.00 3 0 20.0 60.00 60 0.00 0.00 60.0060.00 4 0 20.0 60.00 60 0.00 0.00 60.00 60.00 5 0 20.0 60.00 60 0.000.00 60.00 60.00 6 0 20.0 60.00 60 0.00 0.00 60.00 60.00 7 0 20.0 60.0060 0.00 0.00 60.00 60.00 8 0 20.0 60.00 60 0.00 0.00 60.00 60.00 9 020.0 60.00 60 0.00 0.00 60.00 60.00 10 0 20.0 60.00 90 0.00 0.00 60.0060.00 11 2 22.0 60.10 90 0.10 2.00 62.10 62.00 12 2 24.0 60.30 90 0.203.90 64.20 64.00 13 2 26.0 60.58 90 0.29 5.70 66.29 66.00 14 2 28.060.95 90 0.37 7.42 68.37 68.00 15 2 30.0 61.40 90 0.45 9.05 70.45 70.0016 2 32.0 61.93 90 0.53 10.60 72.53 72.00 17 2 34.0 62.54 90 0.60 12.0774.60 74.00 18 2 36.0 63.21 90 0.67 13.46 76.67 76.00 19 2 38.0 63.95 900.74 14.79 78.74 78.00 20 2 40.0 64.75 90 0.80 16.05 80.80 80.00 21 242.0 65.61 90 0.86 17.25 82.86 82.00 22 2 44.0 66.53 90 0.92 18.39 84.9284.00 23 2 46.0 67.51 90 0.97 19.47 86.97 86.00 24 2 48.0 68.53 90 1.0220.49 89.02 88.00 25 2 50.0 69.61 90 1.07 21.47 91.07 90.00 26 0 50.070.62 90 1.02 20.39 91.02 90.00 27 0 50.0 71.59 90 0.97 19.38 90.9790.00 28 0 50.0 72.51 90 0.92 18.41 90.92 90.00 29 0 50.0 73.39 90 0.8717.49 90.87 90.00 30 0 50.0 74.22 90 0.83 16.61 90.83 90.00 31 0 50.075.01 90 0.79 15.78 90.79 90.00 32 0 50.0 75.76 90 0.75 14.99 90.7590.00 33 0 50.0 76.47 90 0.71 14.24 90.71 90.00 34 0 50.0 77.15 90 0.6813.53 90.68 90.00 35 0 50.0 77.79 90 0.64 12.85 90.64 90.00 36 0 50.078.40 90 0.61 12.21 90.61 90.00 37 0 50.0 78.98 90 0.58 11.60 90.5890.00 38 0 50.0 79.53 90 0.55 11.02 90.55 90.00 39 0 50.0 80.05 90 0.5210.47 90.52 90.00 40 0 50.0 80.55 90 0.50 9.95 90.50 90.00 41 0 50.081.02 90 0.47 9.45 90.47 90.00 42 0 50.0 81.47 90 0.45 8.98 90.45 90.0043 0 50.0 81.90 90 0.43 8.53 90.43 90.00 44 0 50.0 82.30 90 0.41 8.1090.41 90.00 45 0 50.0 82.69 90 0.38 7.70 90.38 90.00 46 0 50.0 83.05 900.37 7.31 90.37 90.00 47 0 50.0 83.40 90 0.35 6.95 90.35 90.00 48 0 50.083.73 90 0.33 6.60 90.33 90.00 49 0 50.0 84.04 90 0.31 6.27 90.31 90.0050 0 50.0 84.34 90 0.30 5.96 90.30 90.00 51 0 50.0 84.63 90 0.28 5.6690.28 90.00 52 0 50.0 84.89 90 0.27 5.37 90.27 90.00 53 0 50.0 85.15 900.26 5.11 90.26 90.00 54 0 50.0 85.39 90 0.24 4.85 90.24 90.00 55 0 50.085.62 90 0.23 4.61 90.23 90.00 56 0 50.0 85.84 90 0.22 4.38 90.22 90.0057 0 50.0 86.05 90 0.21 4.16 90.21 90.00 58 0 50.0 86.25 90 0.20 3.9590.20 90.00 59 0 50.0 86.43 90 0.19 3.75 90.19 90.00 60 0 50.0 86.61 900.18 3.57 90.18 90.00 61 0 50.0 86.78 90 0.17 3.39 90.17 90.00 62 0 50.086.94 90 0.16 3.22 90.16 90.00 63 0 50.0 87.10 90 0.15 3.06 90.15 90.0064 0 50.0 87.24 90 0.15 2.90 90.15 90.00 65 0 50.0 87.38 90 0.14 2.7690.14 90.00 66 0 50.0 87.51 90 0.13 2.62 90.13 90.00 67 0 50.0 87.63 900.12 2.49 90.12 90.00 68 0 50.0 87.75 90 0.12 2.37 90.12 90.00 69 0 50.087.87 90 0.11 2.25 90.11 90.00 70 0 50.0 87.97 90 0.11 2.13 90.11 90.0071 0 50.0 88.07 90 0.10 2.03 90.10 90.00 72 0 50.0 88.17 90 0.10 1.9390.10 90.00 73 0 50.0 88.26 90 0.09 1.83 90.09 90.00 74 0 50.0 88.35 900.09 1.74 90.09 90.00 75 0 50.0 88.43 90 0.08 1.65 90.08 90.00 76 0 50.088.51 90 0.08 1.57 90.08 90.00 77 0 50.0 88.58 90 0.07 1.49 90.07 90.0078 0 50.0 88.65 90 0.07 1.42 90.07 90.00 79 0 50.0 88.72 90 0.07 1.3590.07 90.00 80 0 50.0 88.79 90 0.06 1.28 90.06 90.00 81 0 50.0 88.85 900.06 1.21 90.06 90.00 82 0 50.0 88.90 90 0.06 1.15 90.06 90.00 83 0 50.088.96 90 0.05 1.10 90.05 90.00 84 0 50.0 89.01 90 0.05 1.04 90.05 90.0085 0 50.0 89.06 90 0.05 0.99 90.05 90.00 86 0 50.0 89.11 90 0.05 0.9490.05 90.00 87 0 50.0 89.15 90 0.04 0.89 90.04 90.00 88 0 50.0 89.19 900.04 0.85 90.04 90.00 89 0 50.0 89.23 90 0.04 0.81 90.04 90.00 90 0 50.089.27 90 0.04 0.77 90.04 90.00 91 0 50.0 89.31 90 0.04 0.73 90.04 90.0092 0 50.0 89.34 90 0.03 0.69 90.03 90.00 93 0 50.0 89.38 90 0.03 0.6690.03 90.00 94 0 50.0 89.41 90 0.03 0.62 90.03 90.00 95 0 50.0 89.44 900.03 0.59 90.03 90.00 96 0 50.0 89.47 90 0.03 0.56 90.03 90.00 97 0 50.089.49 90 0.03 0.53 90.03 90.00 98 0 50.0 89.52 90 0.03 0.51 90.03 90.0099 0 50.0 89.54 90 0.02 0.48 90.02 90.00 100 0 50.0 89.56 90 0.02 0.4690.02 90.00

FIGS. 3d and 3e illustrate the effect of rate-time relative to actualprocess response time. In FIG. 3d , the rate-time is 10 minutes (halfthe original value), resulting in modest overshoot and ultimatelyrequiring corrective control moves in the reverse direction. In FIG. 3e, the rate time is 40 minutes (twice the original value), ultimatelyrequiring additional control moves in the same direction, after themoves are initially discontinued. This shows that, while the “ideal”rate-time is equal to process response time, a wide range of rate-timevalues will result in reliable control, which is important becauseactual process response times often vary. In practice, most people willwant the rate-time to be equal to or greater than the actual processresponse time, as gradual control with little or no overshoot, anderring on the side of undershoot, is generally preferred in high levelprocess control. Details of the simulation shown in FIG. 3d are listedin Table 4.

TABLE 4 final min mov DCV ICV Target icvvel veldist velstop icv 0 0 20.060.00 60 0.00 0.00 60.00 60.00 1 0 20.0 60.00 60 0.00 0.00 60.00 60.00 20 20.0 60.00 60 0.00 0.00 60.00 60.00 3 0 20.0 60.00 60 0.00 0.00 60.0060.00 4 0 20.0 60.00 60 0.00 0.00 60.00 60.00 5 0 20.0 60.00 60 0.000.00 60.00 60.00 6 0 20.0 60.00 60 0.00 0.00 60.00 60.00 7 0 20.0 60.0060 0.00 0.00 60.00 60.00 8 0 20.0 60.00 60 0.00 0.00 60.00 60.00 9 020.0 60.00 60 0.00 0.00 60.00 60.00 10 0 20.0 60.00 90 0.00 0.00 60.0060.00 11 1 21.0 60.05 90 0.05 0.50 60.55 61.00 12 1 22.0 60.15 90 0.100.97 61.12 62.00 13 1 23.0 60.29 90 0.14 1.43 61.72 63.00 14 1 24.060.48 90 0.19 1.85 62.33 64.00 15 1 25.0 60.70 90 0.23 2.26 62.96 65.0016 1 26.0 60.97 90 0.26 2.65 63.62 66.00 17 1 27.0 61.27 90 0.30 3.0264.29 67.00 18 1 28.0 61.60 90 0.34 3.37 64.97 68.00 19 1 29.0 61.97 900.37 3.70 65.67 69.00 20 1 30.0 62.38 90 0.40 4.01 66.39 70.00 21 1 31.062.81 90 0.43 4.31 67.12 71.00 22 1 32.0 63.27 90 0.46 4.60 67.86 72.0023 1 33.0 63.75 90 0.49 4.87 68.62 73.00 24 1 34.0 64.27 90 0.51 5.1269.39 74.00 25 1 35.0 64.80 90 0.54 5.37 70.17 75.00 26 1 36.0 65.36 900.56 5.60 70.96 76.00 27 1 37.0 65.94 90 0.58 5.82 71.76 77.00 28 1 38.066.55 90 0.60 6.03 72.57 78.00 29 1 39.0 67.17 90 0.62 6.23 73.40 79.0030 1 40.0 67.81 90 0.64 6.42 74.23 80.00 31 1 41.0 68.47 90 0.66 6.5975.07 81.00 32 1 42.0 69.15 90 0.68 6.76 75.91 82.00 33 1 43.0 69.84 900.69 6.93 76.77 83.00 34 1 44.0 70.55 90 0.71 7.08 77.63 84.00 35 1 45.071.27 90 0.72 7.23 78.50 85.00 36 1 46.0 72.01 90 0.74 7.36 79.37 86.0037 1 47.0 72.76 90 0.75 7.50 80.25 87.00 38 1 48.0 73.52 90 0.76 7.6281.14 88.00 39 1 49.0 74.29 90 0.77 7.74 82.03 89.00 40 1 50.0 75.08 900.79 7.85 82.93 90.00 41 1 51.0 75.87 90 0.80 7.96 83.84 91.00 42 1 52.076.68 90 0.81 8.06 84.74 92.00 43 1 53.0 77.50 90 0.82 8.16 85.66 93.0044 1 54.0 78.32 90 0.83 8.25 86.57 94.00 45 1 55.0 79.16 90 0.83 8.3487.49 95.00 46 1 56.0 80.00 90 0.84 8.42 88.42 96.00 47 1 57.0 80.85 900.85 8.50 89.35 97.00 48 1 58.0 81.71 90 0.86 8.58 90.28 98.00 49 0 58.082.52 90 0.81 8.15 90.67 98.00 50 0 58.0 83.29 90 0.77 7.74 91.03 98.0051 0 58.0 84.03 90 0.74 7.35 91.38 98.00 52 0 58.0 84.73 90 0.70 6.9991.71 98.00 53 0 58.0 85.39 90 0.66 6.64 92.03 98.00 54 0 58.0 86.02 900.63 6.30 92.33 98.00 55 0 58.0 86.62 90 0.60 5.99 92.61 98.00 56 0 58.087.19 90 0.57 5.69 92.88 98.00 57 0 58.0 87.73 90 0.54 5.41 93.14 98.0058 0 58.0 88.24 90 0.51 5.13 93.38 98.00 59 0 58.0 88.73 90 0.49 4.8893.61 98.00 60 0 58.0 89.20 90 0.46 4.63 93.83 98.00 61 0 58.0 89.64 900.44 4.40 94.04 98.00 62 0 58.0 90.05 90 0.42 4.18 94.24 98.00 63 −157.0 90.40 90 0.35 3.47 93.87 97.00 64 −1 56.0 90.68 90 0.28 2.80 93.4896.00 65 −1 55.0 90.90 90 0.22 2.16 93.06 95.00 66 −1 54.0 91.05 90 0.161.55 92.60 94.00 67 −1 53.0 91.15 90 0.10 0.97 92.12 93.00 68 −1 52.091.19 90 0.04 0.43 91.62 92.00 69 −1 51.0 91.18 90 −0.01 −0.10 91.0991.00 70 −1 50.0 91.12 90 −0.06 −0.59 89.98 90.00 71 0 50.0 91.07 90−0.06 −0.56 89.98 90.00 72 0 50.0 91.01 90 −0.05 −0.53 89.98 90.00 73 050.0 90.96 90 −0.05 −0.51 89.98 90.00 74 0 50.0 90.91 90 −0.05 −0.4889.98 90.00 75 0 50.0 90.87 90 −0.05 −0.46 89.98 90.00 76 0 50.0 90.8390 −0.04 −0.43 89.98 90.00 77 0 50.0 90.78 90 −0.04 −0.41 89.98 90.00 780 50.0 90.75 90 −0.04 −0.39 89.98 90.00 79 0 50.0 90.71 90 −0.04 −0.3789.98 90.00 80 0 50.0 90.67 90 −0.04 −0.35 89.98 90.00 81 0 50.0 90.6490 −0.03 −0.34 89.98 90.00 82 0 50.0 90.61 90 −0.03 −0.32 89.98 90.00 830 50.0 90.58 90 −0.03 −0.30 89.98 90.00 84 0 50.0 90.55 90 −0.03 −0.2989.98 90.00 85 0 50.0 90.52 90 −0.03 −0.27 89.98 90.00 86 0 50.0 90.4990 −0.03 −0.26 89.98 90.00 87 0 50.0 90.47 90 −0.02 −0.25 89.98 90.00 880 50.0 90.45 90 −0.02 −0.23 89.98 90.00 89 0 50.0 90.42 90 −0.02 −0.2289.98 90.00 90 0 50.0 90.40 90 −0.02 −0.21 89.98 90.00 91 0 50.0 90.3890 −0.02 −0.20 89.98 90.00 92 0 50.0 90.36 90 −0.02 −0.19 89.98 90.00 930 50.0 90.35 90 −0.02 −0.18 89.98 90.00 94 0 50.0 90.33 90 −0.02 −0.1789.98 90.00 95 0 50.0 90.31 90 −0.02 −0.16 89.98 90.00 96 0 50.0 90.3090 −0.02 −0.16 89.98 90.00 97 0 50.0 90.28 90 −0.01 −0.15 89.98 90.00 980 50.0 90.27 90 −0.01 −0.14 89.98 90.00 99 0 50.0 90.25 90 −0.01 −0.1389.98 90.00 100 0 50.0 90.24 90 −0.01 −0.13 89.98 90.00

Details of the simulation shown in FIG. 3e are listed in Table 5.

TABLE 5 final min mov DCV ICV Target icvvel veldist velstop icv 0 0 20.060.00 60 0.00 0.00 60.00 60.00 1 0 20.0 60.00 60 0.00 0.00 60.00 60.00 20 20.0 60.00 60 0.00 0.00 60.00 60.00 3 0 20.0 60.00 60 0.00 0.00 60.0060.00 4 0 20.0 60.00 60 0.00 0.00 60.00 60.00 5 0 20.0 60.00 60 0.000.00 60.00 60.00 6 0 20.0 60.00 60 0.00 0.00 60.00 60.00 7 0 20.0 60.0060 0.00 0.00 60.00 60.00 8 0 20.0 60.00 60 0.00 0.00 60.00 60.00 9 020.0 60.00 60 0.00 0.00 60.00 60.00 10 0 20.0 60.00 90 0.00 0.00 60.0060.00 11 1 21.0 60.05 90 0.05 2.00 62.05 61.00 12 1 22.0 60.15 90 0.103.90 64.05 62.00 13 1 23.0 60.29 90 0.14 5.71 66.00 63.00 14 1 24.060.48 90 0.19 7.42 67.90 64.00 15 1 25.0 60.70 90 0.23 9.05 69.75 65.0016 1 26.0 60.97 90 0.26 10.60 71.56 66.00 17 1 27.0 61.27 90 0.30 12.0773.33 67.00 18 1 28.0 61.60 90 0.34 13.46 75.07 68.00 19 1 29.0 61.97 900.37 14.79 76.76 69.00 20 1 30.0 62.38 90 0.40 16.05 78.43 70.00 21 131.0 62.81 90 0.43 17.25 80.06 71.00 22 1 32.0 63.27 90 0.46 18.39 81.6572.00 23 1 33.0 63.75 90 0.49 19.47 83.22 73.00 24 1 34.0 64.27 90 0.5120.49 84.76 74.00 25 1 35.0 64.80 90 0.54 21.47 86.27 75.00 26 1 36.065.36 90 0.56 22.39 87.76 76.00 27 1 37.0 65.94 90 0.58 23.28 89.2277.00 28 1 38.0 66.55 90 0.60 24.11 90.66 78.00 29 0 38.0 67.12 90 0.5722.91 90.03 78.00 30 0 38.0 67.66 90 0.54 21.76 89.42 78.00 31 1 39.068.23 90 0.57 22.67 90.90 79.00 32 0 39.0 68.77 90 0.54 21.54 90.3179.00 33 0 39.0 69.28 90 0.51 20.46 89.74 79.00 34 1 40.0 69.82 90 0.5421.44 91.26 80.00 35 0 40.0 70.33 90 0.51 20.37 90.69 80.00 36 0 40.070.81 90 0.48 19.35 90.16 80.00 37 0 40.0 71.27 90 0.46 18.38 89.6580.00 38 1 41.0 71.76 90 0.49 19.46 91.22 81.00 39 0 41.0 72.22 90 0.4618.49 90.71 81.00 40 0 41.0 72.66 90 0.44 17.56 90.22 81.00 41 0 41.073.07 90 0.42 16.69 89.76 81.00 42 1 42.0 73.52 90 0.45 17.85 91.3782.00 43 0 42.0 73.94 90 0.42 16.96 90.90 82.00 44 0 42.0 74.35 90 0.4016.11 90.46 82.00 45 0 42.0 74.73 90 0.38 15.31 90.04 82.00 46 0 42.075.09 90 0.36 14.54 89.63 82.00 47 1 43.0 75.49 90 0.40 15.81 91.3083.00 48 0 43.0 75.86 90 0.38 15.02 90.89 83.00 49 0 43.0 76.22 90 0.3614.27 90.49 83.00 50 0 43.0 76.56 90 0.34 13.56 90.12 83.00 51 0 43.076.88 90 0.32 12.88 89.76 83.00 52 1 44.0 77.24 90 0.36 14.24 91.4784.00 53 0 44.0 77.58 90 0.34 13.52 91.10 84.00 54 0 44.0 77.90 90 0.3212.85 90.75 84.00 55 0 44.0 78.20 90 0.31 12.21 90.41 84.00 56 0 44.078.49 90 0.29 11.60 90.09 84.00 57 0 44.0 78.77 90 0.28 11.02 89.7884.00 58 1 45.0 79.08 90 0.31 12.46 91.54 85.00 59 0 45.0 79.38 90 0.3011.84 91.22 85.00 60 0 45.0 79.66 90 0.28 11.25 90.91 85.00 61 0 45.079.92 90 0.27 10.69 90.61 85.00 62 0 45.0 80.18 90 0.25 10.15 90.3385.00 63 0 45.0 80.42 90 0.24 9.65 90.06 85.00 64 0 45.0 80.65 90 0.239.16 89.81 85.00 65 1 46.0 80.92 90 0.27 10.70 91.62 86.00 66 0 46.081.17 90 0.25 10.17 91.34 86.00 67 0 46.0 81.41 90 0.24 9.66 91.07 86.0068 0 46.0 81.64 90 0.23 9.18 90.82 86.00 69 0 46.0 81.86 90 0.22 8.7290.58 86.00 70 0 46.0 82.07 90 0.21 8.28 90.35 86.00 71 0 46.0 82.26 900.20 7.87 90.13 86.00 72 0 46.0 82.45 90 0.19 7.48 89.92 86.00 73 1 47.082.68 90 0.23 9.10 91.78 87.00 74 0 47.0 82.89 90 0.22 8.65 91.54 87.0075 0 47.0 83.10 90 0.21 8.21 91.31 87.00 76 0 47.0 83.29 90 0.20 7.8091.10 87.00 77 0 47.0 83.48 90 0.19 7.41 90.89 87.00 78 0 47.0 83.65 900.18 7.04 90.70 87.00 79 0 47.0 83.82 90 0.17 6.69 90.51 87.00 80 0 47.083.98 90 0.16 6.36 90.34 87.00 81 0 47.0 84.13 90 0.15 6.04 90.17 87.0082 0 47.0 84.28 90 0.14 5.74 90.01 87.00 83 0 47.0 84.41 90 0.14 5.4589.86 87.00 84 1 48.0 84.59 90 0.18 7.18 91.77 88.00 85 0 48.0 84.76 900.17 6.82 91.58 88.00 86 0 48.0 84.92 90 0.16 6.48 91.40 88.00 87 0 48.085.08 90 0.15 6.15 91.23 88.00 88 0 48.0 85.22 90 0.15 5.85 91.07 88.0089 0 48.0 85.36 90 0.14 5.55 90.92 88.00 90 0 48.0 85.49 90 0.13 5.2890.77 88.00 91 0 48.0 85.62 90 0.13 5.01 90.63 88.00 92 0 48.0 85.74 900.12 4.76 90.50 88.00 93 0 48.0 85.85 90 0.11 4.52 90.37 88.00 94 0 48.085.96 90 0.11 4.30 90.26 88.00 95 0 48.0 86.06 90 0.10 4.08 90.14 88.0096 0 48.0 86.16 90 0.10 3.88 90.04 88.00 97 0 48.0 86.25 90 0.09 3.6889.93 88.00 98 1 49.0 86.39 90 0.14 5.50 91.89 89.00 99 0 49.0 86.52 900.13 5.23 91.74 89.00 100 0 49.0 86.64 90 0.12 4.96 91.61 89.00

Those skilled in the art will recognize that processes with significantdead time pose a potential problem for this method. This is addressed byintroducing the move period for ICVs that have significant dead time,such that DCV values are only updated once per period. This allows forthe effects of the previous move to become evident before makingadditional moves. The period is set approximately equal to (or greaterthan) the overall response time, i.e. dead time plus response time, andmove size is increased to compensate for less frequent moves. FIG. 3fshows the original simulation as with FIG. 3a , but with a 20 minutedead time, having a period of 40 minutes (dead time plus response time),and a move size of 10. When the target increases from 60% to 90% attime=10, the controller commences a DCV move series of +10/period. Thegraph shows the target increase, the increasing DCV, and the delayed ICVresponse. After a series of three step changes the ICV ultimatelysettles at the target value approximately three period (dead time plusresponse time) times later (time=10+(3×40)=ca. 130 minutes). Details ofthe simulation shown in FIG. 3f are listed in Table 6.

TABLE 6 final min mov DCV ICV Target icvvel veldist velstop icv 0 0 20.060.00 60 0.00 0.00 60.00 60.00 1 0 20.0 60.00 60 0.00 0.00 60.00 60.00 20 20.0 60.00 60 0.00 0.00 60.00 60.00 3 0 20.0 60.00 60 0.00 0.00 60.0060.00 4 0 20.0 60.00 60 0.00 0.00 60.00 60.00 5 0 20.0 60.00 60 0.000.00 60.00 60.00 6 0 20.0 60.00 60 0.00 0.00 60.00 60.00 7 0 20.0 60.0060 0.00 0.00 60.00 60.00 8 0 20.0 60.00 60 0.00 0.00 60.00 60.00 9 020.0 60.00 60 0.00 0.00 60.00 60.00 10 0 20.0 60.00 90 0.00 0.00 60.0060.00 11 10 30.0 60.00 90 0.00 0.00 60.00 70.00 12 0 30.0 60.00 90 0.000.00 60.00 70.00 13 0 30.0 60.00 90 0.00 0.00 60.00 70.00 14 0 30.060.00 90 0.00 0.00 60.00 70.00 15 0 30.0 60.00 90 0.00 0.00 60.00 70.0016 0 30.0 60.00 90 0.00 0.00 60.00 70.00 17 0 30.0 60.00 90 0.00 0.0060.00 70.00 18 0 30.0 60.00 90 0.00 0.00 60.00 70.00 19 0 30.0 60.00 900.00 0.00 60.00 70.00 20 0 30.0 60.00 90 0.00 0.00 60.00 70.00 21 0 30.060.00 90 0.00 0.00 60.00 70.00 22 0 30.0 60.00 90 0.00 0.00 60.00 70.0023 0 30.0 60.00 90 0.00 0.00 60.00 70.00 24 0 30.0 60.00 90 0.00 0.0060.00 70.00 25 0 30.0 60.00 90 0.00 0.00 60.00 70.00 26 0 30.0 60.00 900.00 0.00 60.00 70.00 27 0 30.0 60.00 90 0.00 0.00 60.00 70.00 28 0 30.060.00 90 0.00 0.00 60.00 70.00 29 0 30.0 60.00 90 0.00 0.00 60.00 70.0030 0 30.0 60.00 90 0.00 0.00 60.00 70.00 31 0 30.0 60.50 90 0.50 20.0080.50 70.00 32 0 30.0 60.98 90 0.48 19.00 79.98 70.00 33 0 30.0 61.43 900.45 18.05 79.48 70.00 34 0 30.0 61.85 90 0.43 17.15 79.00 70.00 35 030.0 62.26 90 0.41 16.29 78.55 70.00 36 0 30.0 62.65 90 0.39 15.48 78.1270.00 37 0 30.0 63.02 90 0.37 14.70 77.72 70.00 38 0 30.0 63.37 90 0.3513.97 77.33 70.00 39 0 30.0 63.70 90 0.33 13.27 76.97 70.00 40 0 30.064.01 90 0.32 12.60 76.62 70.00 41 0 30.0 64.31 90 0.30 11.97 76.2970.00 42 0 30.0 64.60 90 0.28 11.38 75.97 70.00 43 0 30.0 64.87 90 0.2710.81 75.67 70.00 44 0 30.0 65.12 90 0.26 10.27 75.39 70.00 45 0 30.065.37 90 0.24 9.75 75.12 70.00 46 0 30.0 65.60 90 0.23 9.27 74.86 70.0047 0 30.0 65.82 90 0.22 8.80 74.62 70.00 48 0 30.0 66.03 90 0.21 8.3674.39 70.00 49 0 30.0 66.23 90 0.20 7.94 74.17 70.00 50 0 30.0 66.42 900.19 7.55 73.96 70.00 51 10 40.0 66.59 90 0.18 7.17 73.76 80.00 52 040.0 66.76 90 0.17 6.81 73.58 80.00 53 0 40.0 66.93 90 0.16 6.47 73.4080.00 54 0 40.0 67.08 90 0.15 6.15 73.23 80.00 55 0 40.0 67.23 90 0.155.84 73.07 80.00 56 0 40.0 67.36 90 0.14 5.55 72.91 80.00 57 0 40.067.50 90 0.13 5.27 72.77 80.00 58 0 40.0 67.62 90 0.13 5.01 72.63 80.0059 0 40.0 67.74 90 0.12 4.76 72.50 80.00 60 0 40.0 67.85 90 0.11 4.5272.37 80.00 61 0 40.0 67.96 90 0.11 4.29 72.25 80.00 62 0 40.0 68.06 900.10 4.08 72.14 80.00 63 0 40.0 68.16 90 0.10 3.87 72.03 80.00 64 0 40.068.25 90 0.09 3.68 71.93 80.00 65 0 40.0 68.34 90 0.09 3.50 71.84 80.0066 0 40.0 68.42 90 0.08 3.32 71.74 80.00 67 0 40.0 68.50 90 0.08 3.1671.66 80.00 68 0 40.0 68.58 90 0.07 3.00 71.57 80.00 69 0 40.0 68.65 900.07 2.85 71.50 80.00 70 0 40.0 68.71 90 0.07 2.71 71.42 80.00 71 0 40.069.28 90 0.56 22.57 91.85 80.00 72 0 40.0 69.82 90 0.54 21.44 91.2680.00 73 0 40.0 70.32 90 0.51 20.37 90.69 80.00 74 0 40.0 70.81 90 0.4819.35 90.16 80.00 75 0 40.0 71.27 90 0.46 18.38 89.65 80.00 76 0 40.071.70 90 0.44 17.46 89.17 80.00 77 0 40.0 72.12 90 0.41 16.59 88.7180.00 78 0 40.0 72.51 90 0.39 15.76 88.27 80.00 79 0 40.0 72.89 90 0.3714.97 87.86 80.00 80 0 40.0 73.24 90 0.36 14.22 87.47 80.00 81 0 40.073.58 90 0.34 13.51 87.09 80.00 82 0 40.0 73.90 90 0.32 12.84 86.7480.00 83 0 40.0 74.21 90 0.30 12.20 86.40 80.00 84 0 40.0 74.50 90 0.2911.59 86.08 80.00 85 0 40.0 74.77 90 0.28 11.01 85.78 80.00 86 0 40.075.03 90 0.26 10.46 85.49 80.00 87 0 40.0 75.28 90 0.25 9.93 85.22 80.0088 0 40.0 75.52 90 0.24 9.44 84.95 80.00 89 0 40.0 75.74 90 0.22 8.9784.71 80.00 90 0 40.0 75.95 90 0.21 8.52 84.47 80.00 91 10 50.0 76.16 900.20 8.09 84.25 90.00 92 0 50.0 76.35 90 0.19 7.69 84.04 90.00 93 0 50.076.53 90 0.18 7.30 83.83 90.00 94 0 50.0 76.70 90 0.17 6.94 83.64 90.0095 0 50.0 76.87 90 0.16 6.59 83.46 90.00 96 0 50.0 77.03 90 0.16 6.2683.29 90.00 97 0 50.0 77.17 90 0.15 5.95 83.12 90.00 98 0 50.0 77.32 900.14 5.65 82.97 90.00 99 0 50.0 77.45 90 0.13 5.37 82.82 90.00 100 050.0 77.58 90 0.13 5.10 82.68 90.00

Those skilled in the art will further notice that significant controlcycling about the target value may result, depending on the move size,and this may be especially true of variables with dead time, where movesize is larger to compensate for the longer period. This is remediedwith a smart control deadband, such that when the ICV error (distancefrom target) is below a certain level, then the move size is reducedproportionately, such that move size goes to zero as error goes to zero.

In the context of the present invention, “deadband” would normally implyan error value below which no move is made. This is a common processcontrol concept to reduce or eliminate control cycling or overshoot,i.e. the effect of the move may exceed the size of the current error. A“smart deadband” in this invention means that, instead of this usualdeadband concept, the move size is adjusted based on the ongoing error,so that as the error approaches zero, the move size goes to zero. Thisalso is a method to reduce or eliminate overshoot or cycling. Theequation for a smart move size is as follows:smart movesize=MIN((ABS(error)*k*movesize),movesize)

Where: ABS(error)=absolute value of the error, which is the differencebetween the ICV target and actual value; k=scaling constant, chosen sothat the smart move size equals the normal move size undernormal/expected error conditions; and movesize=normal move size.

The minimum function (MIN) assures that the smart move size neverexceeds the normal move size. This “smart deadband/movesize” techniquecan also be used to effect a larger proportional move size underconditions of a relatively large error, i.e. where a larger controlaction is desired to reduce the large error, and the current error islarge enough that cycling/overshoot is not yet a concern.

Those skilled in the art may point out that this approach is not asprecise as model-based control and may result in excess transient error,i.e. excessive time for the ICV to be brought within limits or to itsoptimum value. However, the inventor believes that the gradual approachcontrol of the present invention, and as reflected in FIGS. 3a through3f , is usually preferred in real process operation, where maintainingprocess stability is always one of the most important priorities.Moreover, although model-based control can be perfect in theory and insimulations where the process response exactly matches the models (theyare one and the same in most simulations), in practice it has been shownto have several shortcomings that lead to poor and unstable performance.The inventor has explained this in published trade journal articles andtrade show presentations.

FIG. 4 diagrams the process flow of the RMC control algorithm 400.Multivariable control typically executes on the order of once perminute. On each execution, it communicates new DCV values to thebase-layer controllers, which typically execute on the order of once persecond. The overall sequence is summarized as follows: Collect currentdata 410; Move logic 420; Rate logic 430; Stability assurance logic 440;and Final move logic 450.

At the start of each execution, necessary data is collected 410 by theapplication. This includes updated live values of the direct controlvariable (DCV) and the indirect control variable (ICV), configuredparameters, such as limits for each variable and the gain signs, andongoing values that are preserved from execution to execution, such asthe period counter and previous ICV value.

The move logic 420 ultimately establishes if the DCV needs a positivemove, a negative move, or no move, based on the current value of theICV, its limits, its optimum value, the sign (positive or negative) ofthe DCV/ICV response, and other factors. In the example, the targetvalue changes from 60% to 90%. This could mean that the low limit wasincreased from 60% to 90%, or that its optimum value changed from 60% to90%. In any case, its target value becomes greater than its currentvalue, and because the DCV/ICV gain is positive, a positive DCV move isneeded. The first priority of the move logic 420 is to keep the ICVwithin its limits, and the second priority is to move the ICV towardsits optimum value, which is usually based on economics. The optimumvalue is often the high or low limit, but in some cases it can be anintermediate value. Note that DCVs also have limits, for example, if apositive move were needed, but the DCV were already at its high limit,then no move would be possible.

Rate logic 430 calculates the rate-of-change (or speed) of the ICV, anddetermines if, at the present speed, it will reach the target valuewithin the rate-time. If so, then the rate logic 430 will ultimatelyoverride any move logic 420 and result in no move.

Stability assurance logic 440 monitors process stability and ifinstability is detected, then it will ultimately override any move logic420 and result in no move. Simple reliable process stability indicatorsinclude rates of change of key variables and deviations (differencebetween setpoint and actual measurement) of single-loop controllers. Formultivariable controllers, an example instability indicator would be ahigh rate of change (positive or negative) of the ICV, or a highdeviation (positive or negative) of the DCV. Other suitable processstability indicators may be used, but this choice encapsulates thesolution within the DCVs and ICVs that are already part of thecontroller.

Stability assurance is the concept that high-level control, such asmultivariable control, should pause or switch off whenever processinstability is detected. This is a further novel feature of the presentinvention as the overall purpose of many high level control applicationsin the past has been to enhance process stability and control, so theidea that they might cause instability and need to be safeguarded hasnever been addressed in industry. The history of MPC, especially asrevealed by the inventor in his several trade journal articles, hasshown the need for this safeguard, because MPC in the prior art hasoften been a source of process instability.

Moreover, stability assurance is also appropriate based on the processcontrol principle that basic process stability is the responsibility ofthe base-layer controls, while high level controls provide optimization.Therefore, under conditions of instability, high level controls shouldpause or switch off regardless of the source of the instability, inorder to allow the base-layer controls to better perform their role ofre-establishing stability, and to avoid high level controls makingpotentially incorrect or further destabilizing moves as a result of theunstable condition of the process.

In the present invention, a Stability Detector can determine if it issafe and prudent to continue implementing the move series. If conditionsare stable, then it is generally considered safe and prudent to proceed.If conditions are unstable, then the changes are paused until stableconditions are re-established, by action of the base-layer controls orby operator intervention, or by natural settling of the process.Stability can be detected in a number of ways. Non-limiting examplesinclude: (a) any temperature reading that exceed a predetermined maximumtemperature value; (b) any pressure reading that exceed a predeterminedmaximum pressure value; (c) a rate-of-change of a key process parameter,such as process pressures or flow rates, that exceed a predeterminedvalue; (d) a high deviation (absolute difference between setpoint andactual value) on any controller.

The final move logic 450 determines and sends the new DCV value to theactual base layer control point 460 based on the combined results of theprevious steps. For example, if move logic 420 indicated a positivemove, rate logic 430 indicated the ICV would not reach its target withinthe rate-time, stability assurance logic 440 indicated the process wasstable, and the base layer DCV was available and within limits, then thefinal move logic 450 would calculate and send a new value to the DCV460. The new value would be the current value plus the move size. Finalmove logic 450 may also incorporate period logic, deadband logic, orother appropriate refinements.

Embodiments

This section describes how RMC can be extended to a multivariablecontrol problem having multiple DCVs and ICVs. Basically, there are twocases to consider: 1) where a DCV affects multiple ICVs, such as allthree DCV's in FIG. 1; and 2) where one ICV is affected by more than oneDCV, such as ICV's 1, 3 and 5 in FIG. 1.

EXAMPLE 1

Where a DCV affects multiple ICVs, then the move logic has to considerall of the ICVs under its control. If any ICV is outside of its limits,then a DCV move is needed. If any ICV is approaching its target value,then the moves are stopped. If all ICVs are within limits, then the DCVcan be used to move itself or one of the ICVs (the one with the highestoptimization priority) towards its optimum target value. If multipleICVs are at or outside their limits and with gains such that the DCV cannot improve one without worsening the other, then the DCV can be movedto control the ICV with the higher control priority, or the DCV can beconfigured to make no move when such a conflict occurs (this latterapproach is typically preferred in operation, since it avoidspotentially making matters worse).

EXAMPLE 2

Where one ICV is affected by multiple DCVs, then the DCV with thegreatest control priority for that ICV would be used, and the other DCVswould ignore that ICV. If the top priority DCV were unavailable orunable to control or optimize the ICV, then the next highest priorityDCV would be used, and so forth. This embodiment describes astraightforward priority scheme to handle multivariable cases. In theinventor's experience, this is adequate, from an optimization,constraint control, and process operation standpoint, in mostapplications. It is possible that in some cases users may prefer to usean alternative hierarchy scheme or a more rigorous optimizer and passresulting target values to the multivariable controller.

The implementation of the present invention is typically done within theprocess control system. The vast majority of process control systems inthe process industries today are distributed control systems (DCSs). Anexample DCS control system architecture is shown in FIG. 2. Basicprocess control functions occur at the controller level, which has veryhigh reliability and speed, but relatively limited functionality. Higherlevel advanced control, as in the present invention, generally takesplace at the supervisory computer level, which has greater functionalcapabilities, but relatively less reliability and speed. Operation,maintenance and engineering functions can take place from variousworkstations.

High level advanced control can also reside apart from the DCS butcommunicatively coupled to enable the communication of information, suchas to a remote real time operating center that can monitor and controlmultiple processes. For example a remote centralized control center canenable the simultaneous monitor and control of multiple processes of anintegrated refining and petrochemical complex.

The present invention can be implemented using function blocks, customfunction blocks, or as a third-party application. Function blocks arebuilt-in functions in the DCS such as math functions, signal processingfunctions, and control algorithm functions, that can be connectedtogether graphically with software and configured in a “fill in theblanks” fashion to accomplish an overall control function. In modernDCSs, built-in function block capabilities are often extensive. Customfunction blocks are re-usable like built-in function blocks, but canencapsulate custom functions programmed by the user that are notprovided by built-in function blocks. This provides a degree ofuser-customized functionality, for applications such as the presentinvention, while preserving the benefits of the function blockenvironment. A “third party application” refers to a software programwritten in a high level language that is hosted at the supervisorycomputer level, and which can be generic to the extent possible, i.e. itis written to work on any modern DCS by replacing only a minimum ofDCS-specific communication interface software components.

In most high level process control applications, including the presentinvention, the complete installation design often includes a combinationof standard function blocks and customization, in the form of eithercustomized function blocks or third-party application software.

Terminology

Advanced controls: These controls are typically added over time afterthe original commissioning of the process unit, to capture some economicor improved performance opportunity. Multivariable control is oneexample. Also known as supervisory or high-level controls, these oftenreside on a supervisory computer as in FIG. 2.

Base-layer (or Basic) controls: These controls provide the basic processoperation and control requirements and typically are part of theoriginal design, construction and commissioning of the process unit.Base-layer controls normally reside at the controller level in FIG. 2.

DCS: Distributed control system, the most common type of control systemin use in the process industries. FIG. 2 illustrates a simplified DCSarchitecture.

DCV: direct control variable, typically a DCS controller setpoint oroutput, such as a flow or temperature controller. Similar to traditionalMPC manipulated variable (MV), but note that in RMC there are nodisturbance variables (DVs), hence the modified terminology.

ICV: indirect control variable, i.e. it is controlled indirectly viaDCV(s), similar to MPC controlled variable (CV)

MPC: multivariable model-based predictive control as is known in theprior art.

“The rate-of-change” of an ICV is the rate (or “speed”) at which the ICVis changing at any point in time. It is typically expressed in units perminute, such as PSIG (units of pressure) per minute, or degreesFahrenheit (units of temperature) per minute. The controller calculatesthe ICV rate of change by using current and recent values and knowingthe time interval between controller iterations. For example, acontroller that executes every five seconds may calculate ICV rate-of-change as the current value, minus the previous value, times 12, toyield rate-of-change per minute. “The estimated time” is the duration,typically in minutes, required for the ICV to reach its target orconstraint value, based on its current value and the currentrate-of-change. For example, if a current ICV value is 100, its currentrate-of-change is 10, and its target value is 110, then the estimatedtime to reach the target would be 1 minute.

The “logic-based directional move solver” means that the method ofcontrol uses a logic algorithm (as opposed to a mathematical algorithm)to determine only the direction to move each DCV. The logic algorithmdoes not determine how fast or ultimately how far to move each DCV, butonly the direction (up or down, positive or negative, etc.). Bycomparison, model-based methods solve for direction, move size, andfinal value in one simultaneous mathematical solution.

“Pre-selected move rates” means that, after the direction for each DCVhas been determined, the DCV is moved at a pre-selected rate (forexample, at 10 units per minute). The actual rate is pre-selected andentered into the controller for each DCV, rather than being calculatedwithin the controller as part of the control algorithm. The number isbased on the user's knowledge and experience regarding safe processoperation and desired operational performance. (Pre-selected move ratescan be thought of as analogous to automobile speed limits, where thepre-selected move rate is not based on the distance to go or onminimizing travel time, but rather is based on road conditions andobserving a safe rate of travel along the way.)

“Rate-based control” (RBC) is an especially novel method, explained indetail in the patent application, to discontinue the DCV moves (aspreviously determined by the directional move-solver and pre-selectedmove rates) predictively, based on the ongoing rate-of-change of relatedICVs relative to their optimization targets and/or constraint limits.Those knowledgeable in process control will perceive, based on the ideaof a directional move solver and pre-selected move rates, that some suchcomplementary mechanism as RBC will be required to prevent undesirableovershoot and oscillatory control behavior from resulting. Said anotherway, RBC is a novel process control technique that makes using therelatively simple concepts of a directional move solver and pre-selectedmove rates possible.

An especially important characteristic of the RBC method is that the RBCmethod is inherently adaptive to, and therefore unaffected by, changesin process gain. This means that reliable control performance isunaffected by changes in process gain, which has been a particular andchronic vulnerability of essentially all other (model-based) controlmethods, which depend on the actual process gain being known (i.e. beingrepresented in the controller by an accurate model). This feature isvery important because actual process response often changesdynamically, so that basing controller parameters on a pre-determined“model” of the process response presents an inherent vulnerability.

Likewise, and also another important feature and claim of this patentapplication, RBC is inherently adaptive to, and therefore unaffected by,changes in the pre-selected move size. This is highly advantageousbecause it means that users can adjust move size to achieve desiredprocess operational performance (to effectively speed up or slow downthe control response) without affecting reliable and stable controlperformance.

Stability assurance is another particular aspect of this disclosure. Itis a different concept than “process control stability” as normallyconsidered in process control theory. Moreover, the concept and methodof stability assurance in the present invention derive from the novelmethod of model-less control of the present invention itself andtherefore are not (cannot be) a logical extension of past claims. Thetopic of process control stability in traditional process control theoryanalyzes control algorithms mathematically and offline to determinecombinations (or “domains”) of controller tuning values and actualprocess gains that become mathematically unstable (typically representedby an oscillatory response with increasing amplitude over time). This isa mathematical analysis used to investigate “windows of valid”(theoretically stable) tuning values for the process or type of processin question, but the control algorithms themselves do not actuallymonitor for process stability in any way, and therefore also do notchange their control action in any way based on detecting instability,as in the present invention. Stability assurance, as defined for thecurrent invention, is a potentially important feature that knowledgeablecontrol engineers may notice. It is possible in the current inventionthat, if actual process gains are unexpectedly high, pre-selected movesare too large, and/or multiple DCVs (affecting the same ICV) are movedsimultaneously, this could lead to an ICV moving faster than desired orexpected. Or, it could lead to a DCV setpoint being moved faster thanthe base layer controller itself can keep up, leading to a highcontroller “deviation” (large unwanted difference between the controllervariable and its setpoint). Due to this potential, it is basically asafeguard feature to include “stability assurance” in the presentinvention, and as uniquely defined for the present invention. Therefore,in the present invention, process instability is uniquely defined ashigh ICV rate of change or high DCV deviation from setpoint; the controlalgorithm itself monitors for process instability based on thesecriteria; and control action is modified (paused) should instabilityactually be detected. This contrasts with prior claims and industryconcepts which deal only with mathematical offline analysis of thecontrol algorithm; do not define criteria for process instability; thecontrol algorithms do not actually monitor for process instabilityonline (in “real-time”); and therefore they also do not take anymodified control action based on instability.

Process control systems generally consist of direct control variables,such as flow controllers, temperature controllers, pressure controllers,level controllers, stream property controllers (such as compositioncontroller, viscosity controller, moisture level controller, etc.) andalso indirect control variables, such as temperatures, pressures andflows that are not directly controlled, but which are affected byvarious process influences, including the direct controllers. Themultiple interactions between the direct controllers (as well as otherindependent variables) and the indirect variables (or dependentvariables) comprise the multivariable nature of most continuousprocesses. The direct control variables can be identified as individualcontrol elements of a unit operation within an overall industrialprocess. Illustrative examples of industrial processes include mineralrefining, oil refining, gas processing, natural gas liquidsfractionation, chemical production, petrochemical production, fossilfueled power generation, nuclear power generation, food processing, andpharmaceutical production processes among others.

System Description

Although the computing unit is shown as having a generalized memory, thecomputing unit typically includes a variety of computer readable media.By way of example, and not limitation, computer readable media maycomprise computer storage media and communication media. The computingsystem memory may include computer storage media in the form of volatileand/or nonvolatile memory such as a read only memory (ROM) and randomaccess memory (RAM). A basic input/output system (BIOS), containing thebasic routines that help to transfer information between elements withinthe computing unit, such as during start-up, is typically stored in ROM.The RAM typically contains data and/or program modules that areimmediately accessible to, and/or presently being operated on by, theprocessing unit. By way of example, and not limitation, the computingunit includes an operating system, application programs, other programmodules, and program data.

The components shown in the memory may also be included in otherremovable/non-removable, volatile/nonvolatile computer storage media.For example only, a hard disk drive may read from or write tonon-removable, nonvolatile magnetic media, a magnetic disk drive mayread from or write to a removable, non-volatile magnetic disk, and anoptical disk drive may read from or write to a removable, nonvolatileoptical disk such as a CD ROM or other optical media. Otherremovable/non-removable, volatile/non-volatile computer storage mediathat can be used in the exemplary operating environment may include, butare not limited to, magnetic tape cassettes, flash memory cards, digitalversatile disks, digital video tape, solid state RAM, solid state ROM,and the like. The drives and their associated computer storage mediadiscussed above therefore, store and/or carry computer readableinstructions, data structures, program modules and other data for thecomputing unit.

A client may enter commands and information into the computing unitthrough the client interface, which may be input devices such as akeyboard and pointing device, commonly referred to as a mouse, trackballor touch pad. Input devices may include a microphone, joystick,satellite dish, scanner, or the like.

These and other input devices are often connected to the processing unitthrough the client interface that is coupled to a system bus, but may beconnected by other interface and bus structures, such as a parallel portor a universal serial bus (USB). A monitor or other type of displaydevice may be connected to the system bus via an interface, such as avideo interface. In addition to the monitor, computers may also includeother peripheral output devices such as speakers and printer, which maybe connected through an output peripheral interface.

For purposes of this disclosure, an information handling system mayinclude any instrumentality or aggregate of instrumentalities operableto compute, classify, process, transmit, receive, retrieve, originate,switch, store, display, manifest, detect, record, reproduce, handle, orutilize any form of information, intelligence, or data for business,scientific, control, or other purposes. For example, an informationhandling system may be a personal computer or tablet device, a cellulartelephone, a network storage device, or any other suitable device andmay vary in size, shape, performance, functionality, and price. Theinformation handling system may include random access memory (RAM), oneor more processing resources such as a central processing unit (CPU) orhardware or software control logic, ROM, and/or other types ofnonvolatile memory. Additional components of the information handlingsystem may include one or more disk drives, one or more network portsfor communication with external devices as well as various input andoutput (I/O) devices, such as a keyboard, a mouse, and a video display.The information handling system may also include one or more busesoperable to transmit communications between the various hardwarecomponents.

For the purposes of this disclosure, computer-readable media may includeany instrumentality or aggregation of instrumentalities that may retaindata and/or instructions for a period of time. Computer-readable mediamay include, for example, without limitation, storage media such as adirect access storage device (e.g., a hard disk drive or floppy diskdrive), a sequential access storage device (e.g., a tape disk drive),compact disk, CD-ROM, DVD, RAM, ROM, electrically erasable programmableread-only memory (EEPROM), and/or flash memory; as well ascommunications media such wires, optical fibers, microwaves, radiowaves, and other electromagnetic and/or optical carriers; and/or anycombination of the foregoing.

The terms “couple” or “couples,” as used herein are intended to meaneither an indirect or a direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection, or through an indirect electrical connection via otherdevices and connections. The term “communicatively coupled” as usedherein is intended to mean coupling of components in a way to permitcommunication of information between the components. Two components maybe communicatively coupled through a wired or wireless communicationnetwork, including but not limited to Ethernet, LAN, fiber optics,radio, microwaves, satellite, internet and the like. Operation and useof such communication networks is well known to those of ordinary skillin the art and will, therefore, not be discussed in detail herein.

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,for example, a computer program tangibly embodied in an informationcarrier, for example, in a machine-readable medium for execution by, orto control the operation of, data processing apparatus, for example, aprogrammable processor, a workstation, a computer, or multiplecomputers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry(e.g., a FPGA or an ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor), or a combinationof permanently and temporarily configured hardware may be a designchoice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 5 is a block diagram of machine in the example form of a computersystem 500 within which instructions, for causing the machine to performany one or more of the methodologies discussed herein, may be executed.In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a set-top box(STB), a PDA, a cellular telephone, a web appliance, a network router,switch or bridge, or any machine capable of executing instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” shall also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

The example computer system 500 includes a processor 502 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) orboth), a main memory 504 and a static memory 506, which communicate witheach other via a bus 508. The computer system 500 may further include avideo display unit 510 (e.g., a liquid crystal display (LCD) or acathode ray tube (CRT)). The computer system 500 also includes analphanumeric input device 512 (e.g., a keyboard), a user interface (UI)navigation device 514 (e.g., a mouse), a disk drive unit 516, a signalgeneration device 518 (e.g., a speaker) and a network interface device520.

Machine-Readable Medium and Machine Readable Storage Medium

The disk drive unit 516 includes a machine-readable medium 522 on whichis stored one or more sets of instructions and data structures (e.g.,software) 524 embodying or used by any one or more of the methodologiesor functions described herein. The instructions 524 may also reside,completely or at least partially, within the main memory 504, staticmemory 506, and/or within the processor 502 during execution thereof bythe computer system 500, the main memory 504 and the processor 502 alsoconstituting machine-readable media.

While the machine-readable medium 522 is shown in an example embodimentto be a single medium, the term “machine-readable medium” may include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions or data structures. The term “machine-readable medium”shall also be taken to include any tangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present invention, or that is capable of storing orencoding data structures used by or associated with such instructions.The term “machine-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media. Specific examples of machine-readable storage mediainclude non-volatile memory, including by way of example, semiconductormemory devices (e.g., Erasable Programmable Read-Only Memory (EPROM),Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. All suchmachine readable storage media are hardware devices suitable for storingdata and/or instructions for a suitable period of time to enable use bythe machine, and are therefore non-transitory.

Transmission Medium

The instructions 524 may further be transmitted or received over acommunications network 526 using a transmission medium. The instructions524 may be transmitted using the network interface device 520 and anyone of a number of well-known transfer protocols (e.g., HTTP). Examplesof communication networks include a LAN, a WAN, the Internet, mobiletelephone networks, Plain Old Telephone (POTS) networks, and wirelessdata networks (e.g., WiFi and WiMax networks). The term “transmissionmedium” shall be taken to include any intangible medium that is capableof storing, encoding or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such software.

Although many other internal components of the computer system are notshown, those of ordinary skill in the art will appreciate that suchcomponents and their interconnections are well known.

Although an embodiment has been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the invention. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense. The accompanying drawings that form a parthereof, show by way of illustration, and not of limitation, specificembodiments in which the subject matter may be practiced. Theembodiments illustrated are described in sufficient detail to enablethose skilled in the art to practice the teachings disclosed herein.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. This Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

Process Description

Process control systems, such as distributed or scalable process controlsystems like those used in hydrocarbon refining or petrochemicalproduction processes, typically include one or more process controllerscommunicatively coupled to each other, to at least one host or operatorworkstation and to one or more field devices via analog, digital orcombined analog/digital buses. The field devices, which may be, forexample, valves, valve positioners, switches and transmitters (e.g.,temperature, pressure and flow rate sensors), perform functions withinthe process such as opening or closing valves and measuring processparameters. The process controller receives signals indicative ofprocess measurements made by the field devices and/or other ofinformation pertaining to the field devices, uses this information toimplement a control routine and then generates control signals which aresent over the buses to the field devices to control the operation of theprocess. Information from the field devices and the controller istypically made available to one or more applications executed by theoperator workstation to enable an operator to perform any desiredfunction with respect to the process, such as viewing the current stateof the process, modifying the operation of the process, etc.

Information from the field devices and the process controllers istypically made available to one or more other hardware devices such asoperator workstations, maintenance workstations, engineer workstations,personal computers, handheld devices, data historians, reportgenerators, centralized databases, etc., to enable an operator,maintenance or engineering person to perform desired functions withrespect to the process such as, for example, changing settings of theprocess control routine, modifying the operation of the control moduleswithin the process controllers or the smart field devices, viewing thecurrent state of the process or of particular devices within the processplant, viewing alarms generated by field devices and processcontrollers, simulating the operation of the process for the purpose oftraining personnel or testing the process control software, diagnosingproblems or hardware failures within the process plant, etc.

These and other diagnostic and optimization applications are typicallyimplemented on a system-wide basis in one or more of the operator,maintenance or engineering workstations, and may provide preconfigureddisplays to the personnel regarding the operating state of the processplant, or the devices and equipment within the process plant. Typicaldisplays include alarming displays that receive alarms generated by theprocess controllers or other devices within the process plant, controldisplays indicating the operating state of the process controllers andother devices within the process plant, maintenance displays indicatingthe operating state of the devices within the process plant, etc.Likewise, these and other diagnostic applications may enable anoperator, maintenance or engineering person to retune a control loop orto reset other control parameters, to run a test on one or more fielddevices to determine the current status of those field devices, tocalibrate field devices or other equipment, or to perform other problemdetection and correction activities on devices and equipment within theprocess plant.

While the invention has been described herein in terms of embodiments,these embodiments are not to be taken as limiting the scope of theinvention. It is deemed to be within the scope of the present inventionthat each embodiment disclosed herein is usable with each and everyother embodiment disclosed herein and that all embodiments disclosedherein are combinable with each other.

Depending on the context, all references herein to the “invention” mayin some cases refer to certain specific embodiments only. In other casesit may refer to subject matter recited in one or more, but notnecessarily all, of the claims. While the foregoing is directed toembodiments, versions and examples of the present invention, which areincluded to enable a person of ordinary skill in the art to make and usethe inventions when the information in this patent is combined withavailable information and technology, the inventions are not limited toonly these particular embodiments, versions and examples. Other andfurther embodiments, versions and examples of the invention may bedevised without departing from the basic scope thereof and the scopethereof is determined by the claims that follow.

What is claimed is:
 1. A method of process control of a unit operation within an industrial process comprising: collecting data sets for a process having an initial base layer Direct Control Variable value and a corresponding initial Indirect Control Variable value, a rate-time value and a move series value; receiving a target Indirect Control Variable value; determining whether a change in Direct Control Variable value is needed; if needed, implementing a move series to the Direct Control Variable value; calculating a rate-of-change of the Indirect Control Variable value and an estimated time needed to reach the target Indirect Control Variable value; comparing the estimated time needed to reach the target Indirect Control Variable value with the rate-time; discontinuing the Direct Control Variable move series when the time needed to reach the target Indirect Control Variable value is less than a predetermined percentage of the rate-time value; wherein the method does not utilize model-based control; wherein the method controls one or more Direct Control Variable selected to be used to control industrial operations from the group consisting of: a flow controller, a temperature controller, a level controller, a pressure controller, and a stream property controller; wherein the one or more Direct Control Variable is an element of a unit operation within an industrial process, the industrial process selected from the group consisting of: mineral refining, oil refining, gas processing, natural gas liquids fractionation, chemical production, petrochemical production, fossil fueled power generation, nuclear power generation, food processing, and pharmaceutical production.
 2. The method of claim 1, further comprising checking process stability.
 3. The method of claim 2, wherein if an unstable condition within the process is detected the move series is temporarily halted until stability is achieved.
 4. The method of claim 1, wherein the method does not utilize non-linear optimization.
 5. The method of claim 1, wherein the method is not affected by changes in process gain.
 6. The method of claim 1, wherein the method is not affected by changes in move size.
 7. The method of claim 1, wherein the method is not affected by complex process interaction dynamics.
 8. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to control a unit operation within an industrial process and perform a method comprising: collecting data sets for a process having an initial base layer Direct Control Variable value and a corresponding initial Indirect Control Variable value, a rate-time value and a move series value; receiving a target ICV value; determining whether a change in Direct Control Variable value is needed; implementing a move series to the Direct Control Variable value; calculating a rate-of-change of the Indirect Control Variable value and an estimated time needed to reach the target Indirect Control Variable value; comparing the estimated time needed to reach the target Indirect Control Variable value with the rate-time; and discontinuing the move series to the Direct Control Variable value when the time needed to reach the target Indirect Control Variable value is less than a predetermined percentage of the rate-time value; wherein the method does not utilize model-based control; wherein the method controls one or more Direct Control Variable selected to be used to control industrial operations from the group consisting of: a flow controller, a temperature controller, a level controller, a pressure controller, and a stream property controller; wherein the one or more Direct Control Variable is an element of a unit operation within an industrial process, the industrial process selected from the group consisting of: mineral refining, oil refining, gas processing, natural gas liquids fractionation, chemical production, petrochemical production, fossil fueled power generation, nuclear power generation, food processing, and pharmaceutical production.
 9. The non-transitory computer-readable medium of claim 8, further causes the processor to perform a method comprising checking process stability.
 10. The non-transitory computer-readable medium of claim 8, wherein if an unstable condition within the process is detected the move series is temporarily halted until stability is achieved.
 11. The non-transitory computer-readable medium of claim 8, wherein the method does not utilize non-linear optimization.
 12. The non-transitory computer-readable medium of claim 8, wherein the method is not affected by changes in process gain.
 13. The non-transitory computer-readable medium of claim 8, wherein the method is not affected by changes in move size.
 14. The non-transitory computer-readable medium of claim 8, wherein the method is not affected by complex process interaction dynamics.
 15. A system to control a unit operation within an industrial process comprising: at least one processor; at least one memory coupled to the at least one processor and storing computer executable instructions for a model-less multivariable process control, the computer executable instructions comprising instructions for: collecting data sets for a process having an initial base layer Direct Control Variable value and a corresponding initial Indirect Control Variable value, a rate-time value and a move series value; receiving a target Indirect Control Variable value; determining whether a change in Direct Control Variable value is needed; implementing a move series to the Direct Control Variable value; calculating a rate-of-change of the Indirect Control Variable value and an estimated time needed to reach the target Indirect Control Variable value; comparing the estimated time needed to reach the target Indirect Control Variable value with the rate-time; and discontinuing the move series to the Direct Control Variable value when the time needed to reach the target Indirect Control Variable value is less than a predetermined percentage of the rate-time value; wherein the method controls one or more Direct Control Variable selected to be used to control industrial operations from the group consisting of: a flow controller, a temperature controller, a level controller, a pressure controller, and a stream property controller; wherein the one or more Direct Control Variable is an element of a unit operation within an industrial process, the industrial process selected from the group consisting of: mineral refining, oil refining, gas processing, natural gas liquids fractionation, chemical production, petrochemical production, fossil fueled power generation, nuclear power generation, food processing, and pharmaceutical production.
 16. The system of claim 15, further comprising a display device coupled to the processor.
 17. The system of claim 15, wherein the computer executable instructions further comprise checking process stability.
 18. The system of claim 17, wherein the computer executable instructions further comprise if an unstable condition within the process is detected, the move series are temporarily halted until stability is achieved. 